class_docstr: Proposes new trials using a linear model.
  
  The model suggests an architecture no deeper than those in the trials.
  Sometimes (by a coin flip) adds one layer of depth to the suggestion.
class_docstr_tok: ["Proposes","new","trials","using","a","linear","model",".","The","model","suggests","an","architecture","no","deeper","than","those","in","the","trials",".","Sometimes","(","by","a","coin","flip",")","adds","one","layer","of","depth","to","the","suggestion","."]
docstr: Pad with empty blocks according to spec network alignment.
docstr_tok: ["Pad","with","empty","blocks","according","to","spec","network","alignment","."]
code: def _pad_architecture(self, arch, maxdepth):
      """Pad with empty blocks according to spec network alignment."""
      empties = [block_builder.BlockType.EMPTY_BLOCK.value] * (
          maxdepth - len(arch)
      )
      align = self._phoenix_spec.linear_model.network_alignment
      if align == phoenix_spec_pb2.LinearModelSpec.NET_ALIGN_BASE:
          return empties + list(arch)
      elif (
          align == phoenix_spec_pb2.LinearModelSpec.NET_ALIGN_HEAD
          or align
          == phoenix_spec_pb2.LinearModelSpec.NET_ALIGN_UNSPECIFIED
      ):
          return list(arch) + empties
      else:
          raise ValueError(
              "Phoenix spec network_alignment unknown value"
          )
  
file_path: model_search/model_search/search/linear_model.py
imports: from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.proto import phoenix_spec_pb2
  from model_search.search import common
  from model_search.search import search_algorithm
  import numpy
  import sklearn
  import sklearn.linear_model
label_desc: Use image node_id 4 for calling the LinearModel obj's underlying member method code with example usage: obj._pad_architecture(arch, maxdepth) and returns: unknown, unknown